In this talk we describe our efforts, as part of the MLbase project, to develop a distributed Machine Learning platform on top of Spark. In particular, we present the details of two core components of MLbase, namely MLlib and MLI, which are scheduled for open-source release this summer. MLlib provides a standard Spark library of scalable algorithms for common learning settings such as classification, regression, collaborative filtering and clustering. MLI is a machine learning API that facilitates the development of new ML algorithms and feature extraction methods. As part of our release, we include a library written against the MLI containing standard and experimental ML algorithms, optimization primitives and feature extraction methods.